{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年3月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in First-Tier Cities 程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and city in ('北京','上海','广州','深圳')  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_description</th>\n",
       "      <th>...</th>\n",
       "      <th>bd_flink</th>\n",
       "      <th>bd_presto</th>\n",
       "      <th>bd_heron</th>\n",
       "      <th>ml_sklearn</th>\n",
       "      <th>ml_mllib</th>\n",
       "      <th>ml_tensorlayer</th>\n",
       "      <th>ml_paddlepaddle</th>\n",
       "      <th>ml_cntk</th>\n",
       "      <th>ml_theano</th>\n",
       "      <th>ml_mahout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13672</th>\n",
       "      <td>116717167</td>\n",
       "      <td>10000</td>\n",
       "      <td>200</td>\n",
       "      <td>海外业务软件工程师（2020届）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9429</th>\n",
       "      <td>114568087</td>\n",
       "      <td>3750</td>\n",
       "      <td>151</td>\n",
       "      <td>C#软件开发工程师（雄安新区岗位）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255</th>\n",
       "      <td>108834444</td>\n",
       "      <td>15000</td>\n",
       "      <td>150</td>\n",
       "      <td>Java开发工程师</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16334</th>\n",
       "      <td>117792359</td>\n",
       "      <td>12500</td>\n",
       "      <td>100</td>\n",
       "      <td>Java中级软件工程师</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>长城计算机软件与系统有限公司（简称长城软件）是中国电子信息产业集团有限公司（简称中国电子）计...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14586</th>\n",
       "      <td>117143097</td>\n",
       "      <td>13541</td>\n",
       "      <td>99</td>\n",
       "      <td>2020届校园招聘（技术类）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>富士康工业互联网股份有限公司是全球领先的通信网络设备、云服务设备、精密工具及工业机器人专业设...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_id  monthly_salary  headcount              title zhinengleibie  \\\n",
       "13672  116717167           10000        200   海外业务软件工程师（2020届）         软件工程师   \n",
       "9429   114568087            3750        151  C#软件开发工程师（雄安新区岗位）         软件工程师   \n",
       "3255   108834444           15000        150          Java开发工程师         软件工程师   \n",
       "16334  117792359           12500        100        Java中级软件工程师         软件工程师   \n",
       "14586  117143097           13541         99     2020届校园招聘（技术类）         软件工程师   \n",
       "\n",
       "      career real_province province city  \\\n",
       "13672  一般程序员          None       上海   上海   \n",
       "9429   一般程序员          None       北京   北京   \n",
       "3255   一般程序员          None       北京   北京   \n",
       "16334  一般程序员          None       北京   北京   \n",
       "14586  算法工程师          None       北京   北京   \n",
       "\n",
       "                                     company_description  ... bd_flink  \\\n",
       "13672  上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...  ...    False   \n",
       "9429   北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...  ...    False   \n",
       "3255   大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...  ...    False   \n",
       "16334  长城计算机软件与系统有限公司（简称长城软件）是中国电子信息产业集团有限公司（简称中国电子）计...  ...    False   \n",
       "14586  富士康工业互联网股份有限公司是全球领先的通信网络设备、云服务设备、精密工具及工业机器人专业设...  ...    False   \n",
       "\n",
       "      bd_presto bd_heron ml_sklearn  ml_mllib  ml_tensorlayer  \\\n",
       "13672     False    False      False     False           False   \n",
       "9429      False    False      False     False           False   \n",
       "3255      False    False      False     False           False   \n",
       "16334     False    False      False     False           False   \n",
       "14586     False    False      False     False           False   \n",
       "\n",
       "       ml_paddlepaddle  ml_cntk  ml_theano  ml_mahout  \n",
       "13672            False    False      False      False  \n",
       "9429             False    False      False      False  \n",
       "3255             False    False      False      False  \n",
       "16334            False    False      False      False  \n",
       "14586            False    False      False      False  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32090, 122)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101390"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5150., 11500., 15000., 20000., 37500.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年3月中国一线城市程序员的平均工资为16619元，工资中位数为15000元，其中95%的人的工资位于5150到37500元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国一线城市程序员的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 16619 Yuan as average, the median is 15000 Yuan, 95% of them earn between 5150 and 37500 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=9384.050777140506, pvalue=0.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(2000, 100), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_93b83f38_5c9b_11ea_9761_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_93b83f38_5c9b_11ea_9761_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow0_col0\" class=\"data row0 col0\" >系统架构师</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow0_col1\" class=\"data row0 col1\" >25642</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow0_col2\" class=\"data row0 col2\" >6339.285714</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow0_col3\" class=\"data row0 col3\" >25000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000.000000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow0_col5\" class=\"data row0 col5\" >4950</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow0_col6\" class=\"data row0 col6\" >4.88%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_93b83f38_5c9b_11ea_9761_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow1_col0\" class=\"data row1 col0\" >算法工程师</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow1_col1\" class=\"data row1 col1\" >21605</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow1_col2\" class=\"data row1 col2\" >5750.000000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow1_col4\" class=\"data row1 col4\" >48000.000000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow1_col5\" class=\"data row1 col5\" >11421</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow1_col6\" class=\"data row1 col6\" >11.26%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_93b83f38_5c9b_11ea_9761_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >0</th>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow2_col0\" class=\"data row2 col0\" >一般程序员</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow2_col1\" class=\"data row2 col1\" >15428</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow2_col2\" class=\"data row2 col2\" >5000.000000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow2_col3\" class=\"data row2 col3\" >14000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow2_col4\" class=\"data row2 col4\" >35000.000000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow2_col5\" class=\"data row2 col5\" >84870</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow2_col6\" class=\"data row2 col6\" >83.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_93b83f38_5c9b_11ea_9761_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow3_col0\" class=\"data row3 col0\" >爬虫工程师</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow3_col1\" class=\"data row3 col1\" >12912</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow3_col2\" class=\"data row3 col2\" >2192.857143</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow3_col4\" class=\"data row3 col4\" >27041.666667</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow3_col5\" class=\"data row3 col5\" >131</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow3_col6\" class=\"data row3 col6\" >0.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_93b83f38_5c9b_11ea_9761_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow4_col0\" class=\"data row4 col0\" >生物信息工程师</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow4_col1\" class=\"data row4 col1\" >11944</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow4_col3\" class=\"data row4 col3\" >13357</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow4_col4\" class=\"data row4 col4\" >15000.000000</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow4_col5\" class=\"data row4 col5\" >18</td>\n",
       "                        <td id=\"T_93b83f38_5c9b_11ea_9761_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.02%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b185b9b588>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16618.574987671367"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101390"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32090.000000\n",
       "mean         3.159551\n",
       "std          3.673775\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          5.000000\n",
       "max        200.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101390"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount_by_cities = data.groupby(by='city').headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "上海    34604\n",
       "北京    18386\n",
       "广州    21094\n",
       "深圳    27306\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount_by_cities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(headcount_by_cities.values, labels=headcount_by_cities.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow0_col0\" class=\"data row0 col0\" >北京</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow0_col1\" class=\"data row0 col1\" >18543</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow0_col2\" class=\"data row0 col2\" >3750</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow0_col3\" class=\"data row0 col3\" >15500</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow0_col4\" class=\"data row0 col4\" >45000</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow0_col5\" class=\"data row0 col5\" >18386</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow0_col6\" class=\"data row0 col6\" >18.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow1_col0\" class=\"data row1 col0\" >上海</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow1_col1\" class=\"data row1 col1\" >17114</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow1_col2\" class=\"data row1 col2\" >5250</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow1_col4\" class=\"data row1 col4\" >37500</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow1_col5\" class=\"data row1 col5\" >34604</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow1_col6\" class=\"data row1 col6\" >34.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow2_col0\" class=\"data row2 col0\" >深圳</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow2_col1\" class=\"data row2 col1\" >16912</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow2_col2\" class=\"data row2 col2\" >6000</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow2_col5\" class=\"data row2 col5\" >27306</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow2_col6\" class=\"data row2 col6\" >26.93%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow3_col0\" class=\"data row3 col0\" >广州</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow3_col1\" class=\"data row3 col1\" >13749</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow3_col2\" class=\"data row3 col2\" >3750</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow3_col5\" class=\"data row3 col5\" >21094</td>\n",
       "                        <td id=\"T_a06a96b6_5c9b_11ea_aeae_701ce71031efrow3_col6\" class=\"data row3 col6\" >20.80%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b1870ec3c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_beijing=data[data.city=='北京'].monthly_salary\n",
    "salary_shanghai=data[data.city=='上海'].monthly_salary\n",
    "salary_guangzhou=data[data.city=='广州'].monthly_salary\n",
    "salary_shenzhen=data[data.city=='深圳'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=105.14462985829374, pvalue=1.3541512935623587e-24)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_beijing, salary_shanghai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=24.465500561790115, pvalue=7.625976927092516e-07)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shanghai, salary_shenzhen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=592.2390094071645, pvalue=1.6535043395609112e-128)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shenzhen, salary_guangzhou)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col1\" class=\"data row0 col1\" >rust</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col2\" class=\"data row0 col2\" >27040</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col3\" class=\"data row0 col3\" >29166</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col4\" class=\"data row0 col4\" >9238</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col5\" class=\"data row0 col5\" >45250</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col6\" class=\"data row0 col6\" >218</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col1\" class=\"data row1 col1\" >scala</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col2\" class=\"data row1 col2\" >22601</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col3\" class=\"data row1 col3\" >20833</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col4\" class=\"data row1 col4\" >8000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col5\" class=\"data row1 col5\" >45000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col6\" class=\"data row1 col6\" >1080</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col1\" class=\"data row2 col1\" >python</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col2\" class=\"data row2 col2\" >21010</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col3\" class=\"data row2 col3\" >18500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col4\" class=\"data row2 col4\" >6250</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col5\" class=\"data row2 col5\" >45000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col6\" class=\"data row2 col6\" >14112</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow2_col7\" class=\"data row2 col7\" >8.79%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col1\" class=\"data row3 col1\" >go</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col2\" class=\"data row3 col2\" >20903</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col3\" class=\"data row3 col3\" >18000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col4\" class=\"data row3 col4\" >7000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col5\" class=\"data row3 col5\" >45000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col6\" class=\"data row3 col6\" >13504</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow3_col7\" class=\"data row3 col7\" >8.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col1\" class=\"data row4 col1\" >lua</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col2\" class=\"data row4 col2\" >20607</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col3\" class=\"data row4 col3\" >20000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col4\" class=\"data row4 col4\" >6218</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col5\" class=\"data row4 col5\" >41666</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col6\" class=\"data row4 col6\" >1482</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow4_col7\" class=\"data row4 col7\" >0.92%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col1\" class=\"data row5 col1\" >r</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col2\" class=\"data row5 col2\" >19944</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col4\" class=\"data row5 col4\" >5231</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col5\" class=\"data row5 col5\" >43437</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col6\" class=\"data row5 col6\" >1335</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col1\" class=\"data row6 col1\" >matlab</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col2\" class=\"data row6 col2\" >19769</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col3\" class=\"data row6 col3\" >18222</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col4\" class=\"data row6 col4\" >5291</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col5\" class=\"data row6 col5\" >40000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col6\" class=\"data row6 col6\" >2621</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow6_col7\" class=\"data row6 col7\" >1.63%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col1\" class=\"data row7 col1\" >typescript</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col2\" class=\"data row7 col2\" >19212</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col3\" class=\"data row7 col3\" >22500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col4\" class=\"data row7 col4\" >3569</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col5\" class=\"data row7 col5\" >35000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col6\" class=\"data row7 col6\" >814</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.51%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col1\" class=\"data row8 col1\" >ruby</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col2\" class=\"data row8 col2\" >18797</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col3\" class=\"data row8 col3\" >17500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col4\" class=\"data row8 col4\" >3525</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col5\" class=\"data row8 col5\" >37500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col6\" class=\"data row8 col6\" >446</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col1\" class=\"data row9 col1\" >perl</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col2\" class=\"data row9 col2\" >18714</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col3\" class=\"data row9 col3\" >17500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col4\" class=\"data row9 col4\" >5000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col5\" class=\"data row9 col5\" >40000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col6\" class=\"data row9 col6\" >1079</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col1\" class=\"data row10 col1\" >swift</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col2\" class=\"data row10 col2\" >17479</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col3\" class=\"data row10 col3\" >16000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col4\" class=\"data row10 col4\" >7288</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col5\" class=\"data row10 col5\" >39275</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col6\" class=\"data row10 col6\" >1089</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col1\" class=\"data row11 col1\" >kotlin</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col2\" class=\"data row11 col2\" >17228</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col3\" class=\"data row11 col3\" >17500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col4\" class=\"data row11 col4\" >7000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col5\" class=\"data row11 col5\" >42937</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col6\" class=\"data row11 col6\" >406</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col1\" class=\"data row12 col1\" >cpp</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col2\" class=\"data row12 col2\" >17143</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col3\" class=\"data row12 col3\" >15000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col4\" class=\"data row12 col4\" >5000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col5\" class=\"data row12 col5\" >40000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col6\" class=\"data row12 col6\" >40795</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow12_col7\" class=\"data row12 col7\" >25.40%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col1\" class=\"data row13 col1\" >haskell</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col2\" class=\"data row13 col2\" >17037</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col3\" class=\"data row13 col3\" >15000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col4\" class=\"data row13 col4\" >11500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col5\" class=\"data row13 col5\" >27000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col6\" class=\"data row13 col6\" >27</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.02%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col1\" class=\"data row14 col1\" >php</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col2\" class=\"data row14 col2\" >16507</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col3\" class=\"data row14 col3\" >14000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col4\" class=\"data row14 col4\" >4994</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col5\" class=\"data row14 col5\" >37500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col6\" class=\"data row14 col6\" >6264</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow14_col7\" class=\"data row14 col7\" >3.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col1\" class=\"data row15 col1\" >java</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col2\" class=\"data row15 col2\" >16274</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col3\" class=\"data row15 col3\" >15000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col4\" class=\"data row15 col4\" >5000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col5\" class=\"data row15 col5\" >37500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col6\" class=\"data row15 col6\" >43664</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow15_col7\" class=\"data row15 col7\" >27.18%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col1\" class=\"data row16 col1\" >objective_c</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col2\" class=\"data row16 col2\" >14279</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col3\" class=\"data row16 col3\" >13000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col4\" class=\"data row16 col4\" >4544</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col5\" class=\"data row16 col5\" >25937</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col6\" class=\"data row16 col6\" >105</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow16_col7\" class=\"data row16 col7\" >0.07%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col1\" class=\"data row17 col1\" >javascript</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col2\" class=\"data row17 col2\" >13850</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col3\" class=\"data row17 col3\" >12500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col5\" class=\"data row17 col5\" >27500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col6\" class=\"data row17 col6\" >15402</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow17_col7\" class=\"data row17 col7\" >9.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col1\" class=\"data row18 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col2\" class=\"data row18 col2\" >13681</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col3\" class=\"data row18 col3\" >12500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col4\" class=\"data row18 col4\" >3750</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col5\" class=\"data row18 col5\" >30000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col6\" class=\"data row18 col6\" >15638</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow18_col7\" class=\"data row18 col7\" >9.74%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col2\" class=\"data row19 col2\" >13077</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col3\" class=\"data row19 col3\" >12500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col4\" class=\"data row19 col4\" >4292</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col5\" class=\"data row19 col5\" >25417</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col6\" class=\"data row19 col6\" >350</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.22%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col1\" class=\"data row20 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col2\" class=\"data row20 col2\" >12696</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col3\" class=\"data row20 col3\" >12250</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col4\" class=\"data row20 col4\" >6250</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col5\" class=\"data row20 col5\" >22500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col6\" class=\"data row20 col6\" >14</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col0\" class=\"data row21 col0\" >22</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col1\" class=\"data row21 col1\" >vba</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col2\" class=\"data row21 col2\" >12629</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col3\" class=\"data row21 col3\" >12000</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col4\" class=\"data row21 col4\" >5262</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col5\" class=\"data row21 col5\" >23987</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col6\" class=\"data row21 col6\" >181</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow21_col7\" class=\"data row21 col7\" >0.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col0\" class=\"data row22 col0\" >23</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col1\" class=\"data row22 col1\" >julia</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col2\" class=\"data row22 col2\" >12500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col3\" class=\"data row22 col3\" >12500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col4\" class=\"data row22 col4\" >12500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col5\" class=\"data row22 col5\" >12500</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col6\" class=\"data row22 col6\" >8</td>\n",
       "                        <td id=\"T_a4d78b3e_5c9b_11ea_ad3b_701ce71031efrow22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b18a66c908>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "初中及以下        9\n",
       "博士         325\n",
       "大专       25175\n",
       "本科       59263\n",
       "硕士        3837\n",
       "高中         172\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_af281f78_5c9b_11ea_90b6_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow0_col1\" class=\"data row0 col1\" >30601</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow0_col2\" class=\"data row0 col2\" >15499</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow0_col5\" class=\"data row0 col5\" >325</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow1_col1\" class=\"data row1 col1\" >21898</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow1_col2\" class=\"data row1 col2\" >4520</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow1_col4\" class=\"data row1 col4\" >46312</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow1_col5\" class=\"data row1 col5\" >3837</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow1_col6\" class=\"data row1 col6\" >4.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow2_col1\" class=\"data row2 col1\" >17560</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow2_col4\" class=\"data row2 col4\" >40000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow2_col5\" class=\"data row2 col5\" >59263</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow2_col6\" class=\"data row2 col6\" >66.75%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow3_col1\" class=\"data row3 col1\" >13880</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow3_col2\" class=\"data row3 col2\" >4297</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow3_col5\" class=\"data row3 col5\" >25175</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow3_col6\" class=\"data row3 col6\" >28.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow4_col0\" class=\"data row4 col0\" >初中及以下</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow4_col1\" class=\"data row4 col1\" >12278</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow4_col2\" class=\"data row4 col2\" >9000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow4_col4\" class=\"data row4 col4\" >17500</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow4_col5\" class=\"data row4 col5\" >9</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow5_col0\" class=\"data row5 col0\" >高中</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow5_col1\" class=\"data row5 col1\" >8336</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow5_col2\" class=\"data row5 col2\" >2800</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow5_col3\" class=\"data row5 col3\" >7000</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow5_col4\" class=\"data row5 col4\" >24700</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow5_col5\" class=\"data row5 col5\" >172</td>\n",
       "                        <td id=\"T_af281f78_5c9b_11ea_90b6_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.19%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b18a684948>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是13661，17677，22371，30774。中位数分别是12500， 16000，20833， 29166。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=61.80903027445163, pvalue=7.504940867541805e-15)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=379.33743792581106, pvalue=9.82658945708907e-84)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1498.525075631907, pvalue=4.2457e-319)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow0_col1\" class=\"data row0 col1\" >31247</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow0_col2\" class=\"data row0 col2\" >12500</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow0_col4\" class=\"data row0 col4\" >55000</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow0_col5\" class=\"data row0 col5\" >399</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.39%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow1_col1\" class=\"data row1 col1\" >22488</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow1_col2\" class=\"data row1 col2\" >11500</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow1_col5\" class=\"data row1 col5\" >14567</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow1_col6\" class=\"data row1 col6\" >14.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow2_col1\" class=\"data row2 col1\" >17968</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow2_col2\" class=\"data row2 col2\" >9000</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow2_col3\" class=\"data row2 col3\" >15500</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow2_col5\" class=\"data row2 col5\" >31378</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow2_col6\" class=\"data row2 col6\" >30.95%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow3_col0\" class=\"data row3 col0\" ></td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow3_col1\" class=\"data row3 col1\" >14534</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow3_col2\" class=\"data row3 col2\" >3500</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow3_col4\" class=\"data row3 col4\" >36000</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow3_col5\" class=\"data row3 col5\" >26951</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow3_col6\" class=\"data row3 col6\" >26.58%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow4_col1\" class=\"data row4 col1\" >13861</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow4_col2\" class=\"data row4 col2\" >5750</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow4_col5\" class=\"data row4 col5\" >28095</td>\n",
       "                        <td id=\"T_b6f9642c_5c9b_11ea_b1b5_701ce71031efrow4_col6\" class=\"data row4 col6\" >27.71%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b185c9b4c8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_type</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow0_col0\" class=\"data row0 col0\" >外资（欧美）</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow0_col1\" class=\"data row0 col1\" >19094</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow0_col2\" class=\"data row0 col2\" >6855</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow0_col4\" class=\"data row0 col4\" >40000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow0_col5\" class=\"data row0 col5\" >5642</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow0_col6\" class=\"data row0 col6\" >5.56%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow1_col0\" class=\"data row1 col0\" >国企</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow1_col1\" class=\"data row1 col1\" >17279</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow1_col2\" class=\"data row1 col2\" >3750</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow1_col5\" class=\"data row1 col5\" >7072</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow1_col6\" class=\"data row1 col6\" >6.98%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow2_col0\" class=\"data row2 col0\" >合资</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow2_col1\" class=\"data row2 col1\" >16946</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow2_col5\" class=\"data row2 col5\" >8533</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow2_col6\" class=\"data row2 col6\" >8.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow3_col0\" class=\"data row3 col0\" >民营公司</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow3_col1\" class=\"data row3 col1\" >16372</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow3_col2\" class=\"data row3 col2\" >5000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow3_col4\" class=\"data row3 col4\" >37500</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow3_col5\" class=\"data row3 col5\" >73744</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow3_col6\" class=\"data row3 col6\" >72.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow4_col0\" class=\"data row4 col0\" >非营利组织</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow4_col1\" class=\"data row4 col1\" >16299</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow4_col2\" class=\"data row4 col2\" >4867</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow4_col3\" class=\"data row4 col3\" >12875</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow4_col4\" class=\"data row4 col4\" >39333</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow4_col5\" class=\"data row4 col5\" >68</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.07%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow5_col0\" class=\"data row5 col0\" >外资（非欧美）</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow5_col1\" class=\"data row5 col1\" >16254</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow5_col2\" class=\"data row5 col2\" >5770</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow5_col3\" class=\"data row5 col3\" >14000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow5_col4\" class=\"data row5 col4\" >37500</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow5_col5\" class=\"data row5 col5\" >5754</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow5_col6\" class=\"data row5 col6\" >5.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow6_col0\" class=\"data row6 col0\" >事业单位</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow6_col1\" class=\"data row6 col1\" >14904</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow6_col2\" class=\"data row6 col2\" >6804</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow6_col3\" class=\"data row6 col3\" >14583</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow6_col4\" class=\"data row6 col4\" >35000</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow6_col5\" class=\"data row6 col5\" >533</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow6_col6\" class=\"data row6 col6\" >0.53%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow7_col0\" class=\"data row7 col0\" >外企代表处</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow7_col1\" class=\"data row7 col1\" >12545</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow7_col2\" class=\"data row7 col2\" >1750</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow7_col3\" class=\"data row7 col3\" >16136</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow7_col4\" class=\"data row7 col4\" >22900</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow7_col5\" class=\"data row7 col5\" >44</td>\n",
       "                        <td id=\"T_ba113b86_5c9b_11ea_8edd_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.04%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b18e627d08>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_size</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000+</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow0_col1\" class=\"data row0 col1\" >24938</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow0_col3\" class=\"data row0 col3\" >22500</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow0_col5\" class=\"data row0 col5\" >7011</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow0_col6\" class=\"data row0 col6\" >6.91%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow1_col0\" class=\"data row1 col0\" >5000-10000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow1_col1\" class=\"data row1 col1\" >19293</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow1_col3\" class=\"data row1 col3\" >16000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow1_col5\" class=\"data row1 col5\" >2665</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow1_col6\" class=\"data row1 col6\" >2.63%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow2_col0\" class=\"data row2 col0\" ></td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow2_col1\" class=\"data row2 col1\" >17461</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow2_col2\" class=\"data row2 col2\" >6250</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow2_col3\" class=\"data row2 col3\" >15500</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow2_col4\" class=\"data row2 col4\" >37744</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow2_col5\" class=\"data row2 col5\" >581</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow2_col6\" class=\"data row2 col6\" >0.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow3_col0\" class=\"data row3 col0\" >500-1000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow3_col1\" class=\"data row3 col1\" >17141</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow3_col2\" class=\"data row3 col2\" >5250</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow3_col5\" class=\"data row3 col5\" >10064</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow3_col6\" class=\"data row3 col6\" >9.93%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow4_col0\" class=\"data row4 col0\" >1000-5000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow4_col1\" class=\"data row4 col1\" >17103</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow4_col2\" class=\"data row4 col2\" >6000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow4_col3\" class=\"data row4 col3\" >15499</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow4_col4\" class=\"data row4 col4\" >37500</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow4_col5\" class=\"data row4 col5\" >12920</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow4_col6\" class=\"data row4 col6\" >12.74%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow5_col0\" class=\"data row5 col0\" >150-500</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow5_col1\" class=\"data row5 col1\" >16251</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow5_col4\" class=\"data row5 col4\" >35000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow5_col5\" class=\"data row5 col5\" >24277</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow5_col6\" class=\"data row5 col6\" >23.94%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow6_col0\" class=\"data row6 col0\" >50-150</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow6_col1\" class=\"data row6 col1\" >15339</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow6_col2\" class=\"data row6 col2\" >4500</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow6_col3\" class=\"data row6 col3\" >14000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow6_col4\" class=\"data row6 col4\" >35000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow6_col5\" class=\"data row6 col5\" >30385</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow6_col6\" class=\"data row6 col6\" >29.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow7_col0\" class=\"data row7 col0\" >50-</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow7_col1\" class=\"data row7 col1\" >14419</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow7_col2\" class=\"data row7 col2\" >4000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow7_col3\" class=\"data row7 col3\" >12500</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow7_col4\" class=\"data row7 col4\" >35000</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow7_col5\" class=\"data row7 col5\" >13487</td>\n",
       "                        <td id=\"T_bb7f2090_5c9b_11ea_8778_701ce71031efrow7_col6\" class=\"data row7 col6\" >13.30%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b1846a30c8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_bce6637a_5c9b_11ea_af27_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >industry</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow0_col0\" class=\"data row0 col0\" >logistic</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow0_col1\" class=\"data row0 col1\" >19107</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow0_col2\" class=\"data row0 col2\" >5680</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow0_col3\" class=\"data row0 col3\" >15424</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow0_col4\" class=\"data row0 col4\" >48000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow0_col5\" class=\"data row0 col5\" >1566</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow0_col6\" class=\"data row0 col6\" >1.54%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow1_col0\" class=\"data row1 col0\" >finance</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow1_col1\" class=\"data row1 col1\" >18092</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow1_col2\" class=\"data row1 col2\" >5250</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow1_col4\" class=\"data row1 col4\" >36167</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow1_col5\" class=\"data row1 col5\" >3472</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow1_col6\" class=\"data row1 col6\" >3.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow2_col0\" class=\"data row2 col0\" >computer</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow2_col1\" class=\"data row2 col1\" >16775</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow2_col2\" class=\"data row2 col2\" >5000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow2_col5\" class=\"data row2 col5\" >77207</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow2_col6\" class=\"data row2 col6\" >76.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow3_col0\" class=\"data row3 col0\" >edu</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow3_col1\" class=\"data row3 col1\" >16613</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow3_col2\" class=\"data row3 col2\" >6500</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow3_col4\" class=\"data row3 col4\" >38150</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow3_col5\" class=\"data row3 col5\" >5848</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow3_col6\" class=\"data row3 col6\" >5.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow4_col0\" class=\"data row4 col0\" >service</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow4_col1\" class=\"data row4 col1\" >15630</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow4_col2\" class=\"data row4 col2\" >6500</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow4_col5\" class=\"data row4 col5\" >572</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.56%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow5_col0\" class=\"data row5 col0\" >trade</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow5_col1\" class=\"data row5 col1\" >15245</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow5_col3\" class=\"data row5 col3\" >14000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow5_col4\" class=\"data row5 col4\" >30000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow5_col5\" class=\"data row5 col5\" >5739</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow5_col6\" class=\"data row5 col6\" >5.66%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow6_col0\" class=\"data row6 col0\" >ads</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow6_col1\" class=\"data row6 col1\" >14906</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow6_col2\" class=\"data row6 col2\" >5889</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow6_col3\" class=\"data row6 col3\" >13929</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow6_col4\" class=\"data row6 col4\" >29166</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow6_col5\" class=\"data row6 col5\" >1067</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow6_col6\" class=\"data row6 col6\" >1.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow7_col0\" class=\"data row7 col0\" >gov</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow7_col1\" class=\"data row7 col1\" >14893</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow7_col2\" class=\"data row7 col2\" >3725</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow7_col3\" class=\"data row7 col3\" >13500</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow7_col4\" class=\"data row7 col4\" >32827</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow7_col5\" class=\"data row7 col5\" >1285</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow7_col6\" class=\"data row7 col6\" >1.27%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow8_col0\" class=\"data row8 col0\" >realestate</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow8_col1\" class=\"data row8 col1\" >14851</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow8_col2\" class=\"data row8 col2\" >4712</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow8_col3\" class=\"data row8 col3\" >12500</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow8_col4\" class=\"data row8 col4\" >37188</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow8_col5\" class=\"data row8 col5\" >1385</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow8_col6\" class=\"data row8 col6\" >1.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow9_col0\" class=\"data row9 col0\" >medical</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow9_col1\" class=\"data row9 col1\" >14777</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow9_col2\" class=\"data row9 col2\" >3750</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow9_col3\" class=\"data row9 col3\" >14000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow9_col4\" class=\"data row9 col4\" >30000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow9_col5\" class=\"data row9 col5\" >2197</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow9_col6\" class=\"data row9 col6\" >2.17%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow10_col0\" class=\"data row10 col0\" >energy</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow10_col1\" class=\"data row10 col1\" >14624</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow10_col2\" class=\"data row10 col2\" >3750</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow10_col3\" class=\"data row10 col3\" >14722</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow10_col4\" class=\"data row10 col4\" >30000</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow10_col5\" class=\"data row10 col5\" >1052</td>\n",
       "                        <td id=\"T_bce6637a_5c9b_11ea_af27_701ce71031efrow10_col6\" class=\"data row10 col6\" >1.04%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b18e6f2f08>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
